Rank tracking by keyword cluster

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavior: the return shape (one row per cluster) and the retention-window constraint on dates. It stops short of noting pagination or error behavior, but adds clear value over the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose and return shape, followed by the constraint and example questions. It is slightly longer than necessary given the trailing example questions, but no sentence is wasted and structure is logical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only analytics tool with annotations covering safety and no output schema, the description usefully conveys the return granularity and the date constraint. However, with 8 parameters at 38% coverage and no output schema, the engines/countries/ungrouped-query filters remain undocumented, leaving an agent without guidance on several optional inputs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 38%, so the description must compensate. It clarifies the project scoping, date window, and cluster grouping (queryClusterIds), but leaves engines, countries, and includeUngroupedQueries unexplained in either place, so the compensation is only partial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('rank-tracking metrics broken down per keyword cluster') and even describes the output granularity ('one row per cluster with its positions, share of voice and sentiment'). The 'cluster' framing distinguishes it from the flat metrics siblings, but it never names or contrasts against get_sentiment_breakdown or get_rank_tracking_time_series, so differentiation is left to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete context with example questions ('which keyword clusters are strongest or weakest') and states a precondition ('dates must fall within the data retention window'). However, it offers no explicit exclusions or named alternatives, so an agent must infer when to prefer it over the closely related breakdown/time-series siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.